Abstract
Forecasting monthly precipitation in arid and semi-arid regions is investigated by feed forward back-propa gation (FFBP), radial basis function, and generalized regression artificial neural networks (ANNs). The ANN models are improved by incorporating a Markov chain-based algorithm (MC-ANNs) with which the percentage of dry months is determined such that the non-physical negative values of precipitation generated by ANN models are eliminated. Monthly precipitation data from three meteorological stations in Jordan are used for case studies. The MC-ANN models are compared based on determination coefficient, mean square error, percentage of dry months and additional performance criteria. A comparison to ANN models without MC incorporated is also made. It is concluded that the MC-ANN models are slightly better than ANN models without MC in forecasting monthly precipitation while they are found appropriate in preserving the percentage of dry months to prevent generation of non-physical negative precipitation.
Similar content being viewed by others
References
Ahrens B.: Distance in spatial interpolation of daily rain gauge data. Hydrol. Earth Syst. Sci. 10(2), 197–208 (2006)
Ciach G.J.: Local random errors in tipping-bucket rain gauge measurements. J Atmospheric Ocean Technol. 20(5), 752–759 (2002)
Villarini, G.; Mandapaka, P.V.; Krajewski, W.F.; Moore, R.J.: Rainfall and sampling uncertainties: a rain gauge perspective. J Geophys. Res. 113, D11102 (2008). doi:10.1029/2007JD009214
Shehadeh N.: The variability of rainfall in Jordan. Dirasat–Humanities. 111(3), 67–84 (1976)
Freiwan M., Kadioglu M.: Climate variability in Jordan. Int. J Climatol. 28(1), 69–89 (2008)
Tarawneh, Q.; Kadioglu, M.: An analysis of precipitation climatology in Jordan. Theor. Appl. Climatol. 74(1–2), 123–136 (2003)
Dahamsheh, A.; Aksoy, H.: Structural characteristics of annual precipitation data in Jordan. Theor. Appl. Climatol. 88(3–4), 201–212 (2007)
Freiwan, M.; Kadioglu, M.: Spatial and temporal analysis of climatological data in Jordan. Int. J Climatol. 28(4), 521–535 (2008)
Freiwan M., Cigizoglu H.K.: Prediction of total monthly rainfall in Jordan using feed forward back propagation method. Fresenius Environ. Bull. 14(2), 142–151 (2005)
Dahamsheh, A.; Aksoy, H.: Artificial neural network models for forecasting intermittent monthly precipitation in arid regions. Meteorol. Appl. 16(3), 325–337 (2009)
Aksoy H., Dahamsheh A.: Artificial neural network models for forecasting monthly precipitation in Jordan. Stoch. Environ. Res. Risk Assess. 23(7), 917–931 (2009)
Han H., Felker P.: Estimation of daily soil water evaporation using an artificial neural network. J. Arid Environ. 37(2), 251–260 (1997)
Yang, Z.P.; Lu, W.X.; Long, Y.Q.; Li, P.: Application and comparison of two prediction models for groundwater levels: a case study in Western Jilin Province, China. J Arid Environ. 73(4–5), 487–492 (2009)
Al-Kharabsheh, A.: Ground-water modeling and long-term management of the Azraq basin as an example of arid area conditions (Jordan). J Arid Environ. 44(2), 143–153 (2000)
Dawson C.W., Wilby R.L.: Hydrological modelling using artificial neural networks. Prog. Phys. Geogr. 25(1), 80–108 (2001)
Demirel, M.C., Venancio, A., Kahya, E.: Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Adv. Eng. Softw., 40(7), 467–473 (2009)
Lange, N.T.: New mathematical approaches in hydrological modeling: an application of artificial neural networks. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 24(1–2), 31–35 (1999)
Mohammadi, H.; Rahmannejad, R.: The estimation of rock mass deformation modulus using regression and artificial neural networks analysis. Arab. J Sci. Eng. 35(1A), 205–217 (2010)
Srinivasulu S., Jain A.: A comparative analysis of training methods for artificial neural network rainfall-runoff models. Appl. Soft Comput. 6(3), 295–306 (2006)
Toth, E.; Brath, A.; Montanari, A.: Comparison of short-term rainfall prediction models for real-time flood forecasting. J. Hydrol. 239(1–4): 132–147 (2000)
Haykins, S.: Neural networks: a comprehensive foundation. Pearson Education Inc., p. 823 (1999)
Cigizoglu, H.K.: Incorporation of ARMA models into flow forecasting by artificial neural networks. Environmetrics. 14(4), 417–427 (2003)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dahamsheh, A., Aksoy, H. Markov Chain-Incorporated Artificial Neural Network Models for Forecasting Monthly Precipitation in Arid Regions. Arab J Sci Eng 39, 2513–2524 (2014). https://doi.org/10.1007/s13369-013-0810-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-013-0810-z